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Unsupervised anomaly detection in production lines

: Graß, Alexander; Beecks Christian; Carvajal Soto, Jose Angel

Volltext urn:nbn:de:0011-n-4972215 (291 KByte PDF)
MD5 Fingerprint: b5e7107b8ee682483eeb0c75cbc3d031
Erstellt am: 29.6.2018

Beyerer, J.:
Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2018 : Selected papers from the International Conference ML4CPS 2018, Karlsruhe, October 23rd and 24th, 2018
Berlin: Springer Vieweg, 2019 (Technologies for Intelligent Automation 9)
ISBN: 978-3-662-58484-2 (Print)
ISBN: 978-3-662-58485-9 (Online)
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <4, 2018, Karlsruhe>
European Commission EC
H2020; 723145; COMPOSITION
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()

With an ongoing digital transformation towards industry 4.0 and the corresponding growth of collected sensor data based on cyber-physical systems, the need for automatic data analysis in industrial production lines has increased drastically. One relevant application scenario is the usage of intelligent approaches to anticipate upcoming failures for maintenance. In this paper, we present a novel approach for anomaly detection regarding predictive maintenance in an industrial data-intensive environment. In particular, we are focusing on historical sensor data from a real reow oven that is used for soldering surface mount electronic components to printed circuit boards. The sensor data, which is provided within the scope of the EU-Project COMPOSITION (under grant no. 723145), comprises information about the heat and the power consumption of individual fans inside a reow oven. The data set contains time-annotated sensor measurements in combination with additional process information over a period of more than seven years.